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Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 1301–1325 of 1718 papers

TitleStatusHype
Sequential Communication in Multi-Agent Reinforcement Learning—0
Surprise Minimizing Multi-Agent Learning with Energy-based Models—0
Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning—0
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning—0
LPMARL: Linear Programming based Implicit Task Assigment for Hiearchical Multi-Agent Reinforcement Learning—0
Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward—0
DSDF: Coordinated look-ahead strategy in stochastic multi-agent reinforcement learning—0
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning—0
LINDA: Multi-Agent Local Information Decomposition for Awareness of Teammates—0
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning—0
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning—0
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures—0
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain—0
DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning—0
On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)—0
On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games—0
Multi-agent Natural Actor-critic Reinforcement Learning Algorithms—0
Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning—0
Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents—0
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning—0
Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning—0
Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning—0
Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified